My first pet ML project, so please pardon if I phrase something incorrectly. Recently I had IMDB sentiment analysis binary classification practice on Tensorflow site. Now I am keen to do multiple label classification of the abstracts in the newspapers.
I have prepared a Pandas dataframe with 2 key columns [sectors_array] and [text]. It is 10 0000 rows. There are 100 sectors overall. [sectors_array] column has 1 to 4 sectors (Same article can fall into multiple categories). Text is a string up to 500 chars. Text contains a piece of article and sectors categorize the text, i.e whether it is food, sport, cinema, politics etc.
So far I cleansed text column to remove single characters, urls, punctuation and did removal of stop words and Lemmatisation for nltk tokenized text.
For multiple sectors I added a hundred of columns with 1/0 flags for each sector.
Now what could be my direction from there in order to classify the new text to existing sector(s)? Would this big number of sectors present an issue for categorisation?
Which library should be best for the task? Tensorflow/Spacy?
Ideally I would want to present up to most probable 4 sectors for the piece of text.